Demonstration of the double Q^2-rescaling model

In this paper we have demonstrated the double Q^2-rescaling model (DQ^2RM) of parton distribution functions of nucleon bounded in nucleus. With different x-region of l-A deep inelastic scattering process we take different approach: in high x-region (0.1\le x\le 0.7) we use the distorted QCD vacuum model which resulted from topologically multi -connected domain vacuum structure of nucleus; in low x-region (10^{-4}\le x\le10^{-3}) we adopt the Glauber (Mueller) multi- scattering formula for gluon coherently rescattering in nucleus. From these two approach we justified the rescaling parton distribution functions in bound nucleon are in agreement well with those we got from DQ^2RM, thus the validity for this phenomenologically model are demonstrated.Comment: 19 page, RevTex, 5 figures in postscrip

J/ψ+jetJ/\psi + jet diffractive production in the direct photon process at HERA

We present a study of $J/\psi + jet$ diffractive production in the direct photon process at HERA based on the factorization theorem for lepton-induced hard diffractive scattering and the factorization formalism of the nonrelativistic QCD (NRQCD) for quarkonia production. Using the diffractive gluon distribution function extracted from HERA data on diffractive deep inelastic scattering and diffractive dijet photon production, we show that this process can be studied at HERA with present integrated luminosity, and can give valuable insights in the color-octet mechanism for heavy quarkonia production.Comment: Revtex, 21 pages, 7 EPS figure

KFC: Kinship Verification with Fair Contrastive Loss and Multi-Task Learning

Kinship verification is an emerging task in computer vision with multiple potential applications. However, there's no large enough kinship dataset to train a representative and robust model, which is a limitation for achieving better performance. Moreover, face verification is known to exhibit bias, which has not been dealt with by previous kinship verification works and sometimes even results in serious issues. So we first combine existing kinship datasets and label each identity with the correct race in order to take race information into consideration and provide a larger and complete dataset, called KinRace dataset. Secondly, we propose a multi-task learning model structure with attention module to enhance accuracy, which surpasses state-of-the-art performance. Lastly, our fairness-aware contrastive loss function with adversarial learning greatly mitigates racial bias. We introduce a debias term into traditional contrastive loss and implement gradient reverse in race classification task, which is an innovative idea to mix two fairness methods to alleviate bias. Exhaustive experimental evaluation demonstrates the effectiveness and superior performance of the proposed KFC in both standard deviation and accuracy at the same time.Comment: Accepted by BMVC 202

Poly[[tetra­aquadi-μ3-oxalato-μ2-oxalato-diprasedymium(III)] dihydrate]

In the title compound, {[Pr2(C2O4)3(H2O)4]·2H2O}n, the three-dimensional network structure has the PrIII ion coordinated by nine O atoms in a distorted tricapped trigonal-prismatic geometry. The coordinated and uncoordinated water mol­ecules inter­act with the carboxyl­ate O atoms to consolidate the network via O—H⋯O hydrogen bonds